Unmasking burnout: exhaustion patterns among at risk health sciences students at the Medical University of Gdańsk, Poland

The primary objective of this study was to integrate several robust statistical techniques to address specific challenges inherent to the analysis of a large, multidimensional clinical dataset characterizing exhaustion in the undergraduate health sciences students population of the Medical University of Gdańsk (MUG). A cross-sectional internet survey was conducted using anonymized questionnaires, including the Patient Health Questionnaire (PHQ-9), Cut-Annoyed-Guilty-Eye (CAGE), and Oldenburg Burnout Inventory (OLBI). To identify factors influencing exhaustion levels measured as OLBI exhaustion domain scores a robust multivariate regression analysis was performed. The analysis included 864 students aged 17–37 years with a median OLBI score of 43.0. A positive relationship between the OLBI disengagement domain score and exhaustion was observed and higher PHQ-9 scores associated strongly with exhaustion. International students, especially the female ones, were more affected by exhaustion than their Polish peers. Additionally, students, who contemplated dropping out of their course due to study related stress showed a trend towards higher exhaustion scores. The employment during studies and potential problematic alcohol use did not significantly affect exhaustion levels. This study revealed a high prevalence of exhaustion among students at MUG, particularly among international students and those with higher depressive symptom scores. By effectively managing outliers, ensuring model stability, and incorporating a broad range of covariates and interactions, this analysis not only provided novel insights into the predictors of burnout but also proposed a methodological way for future research in similar domains.